Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations94
Missing cells19
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory145.4 B

Variable types

Text1
Categorical3
Numeric14

Alerts

Fruitset_marked is highly overall correlated with Fruitset_total and 2 other fieldsHigh correlation
Fruitset_total is highly overall correlated with Fruitset_markedHigh correlation
Marked_flower is highly overall correlated with Marked_fruit and 5 other fieldsHigh correlation
Marked_fruit is highly overall correlated with Fruitset_marked and 5 other fieldsHigh correlation
Seeds_unmarked is highly overall correlated with Total_fruit and 1 other fieldsHigh correlation
Sum of seed_marked is highly overall correlated with Fruitset_marked and 4 other fieldsHigh correlation
Supp_flower is highly overall correlated with TreatmentHigh correlation
Total_fruit is highly overall correlated with Marked_flower and 7 other fieldsHigh correlation
Total_seed is highly overall correlated with Marked_flower and 4 other fieldsHigh correlation
Treatment is highly overall correlated with Supp_flowerHigh correlation
Unmarked_flower is highly overall correlated with Marked_flower and 2 other fieldsHigh correlation
Unmarked_fruit is highly overall correlated with Seeds_unmarked and 1 other fieldsHigh correlation
total_flower is highly overall correlated with Marked_flower and 4 other fieldsHigh correlation
Unmarked_fruit has 1 (1.1%) missing values Missing
Total_fruit has 1 (1.1%) missing values Missing
Fruitset_total has 1 (1.1%) missing values Missing
Sum of seed_marked has 1 (1.1%) missing values Missing
Seeds_unmarked has 7 (7.4%) missing values Missing
Total_seed has 8 (8.5%) missing values Missing
Genotype has unique values Unique
Average of Flower_size has unique values Unique
Average of bp_area has unique values Unique
Unmarked_flower has 25 (26.6%) zeros Zeros
Supp_flower has 72 (76.6%) zeros Zeros
Marked_fruit has 25 (26.6%) zeros Zeros
Total_fruit has 13 (13.8%) zeros Zeros
Fruitset_total has 13 (13.8%) zeros Zeros
Fruitset_marked has 25 (26.6%) zeros Zeros
Sum of seed_marked has 25 (26.6%) zeros Zeros
Seeds_unmarked has 49 (52.1%) zeros Zeros
Total_seed has 14 (14.9%) zeros Zeros

Reproduction

Analysis started2025-05-17 09:40:29.104914
Analysis finished2025-05-17 09:40:48.576294
Duration19.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Genotype
Text

Unique 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2025-05-17T12:40:48.779654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0106383
Min length4

Characters and Unicode

Total characters377
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)100.0%

Sample

1st rowK135
2nd rowK136
3rd rowK139
4th rowK140
5th rowK142
ValueCountFrequency (%)
k135 1
 
1.1%
k136 1
 
1.1%
k139 1
 
1.1%
k140 1
 
1.1%
k142 1
 
1.1%
k147 1
 
1.1%
k155 1
 
1.1%
k157 1
 
1.1%
k159 1
 
1.1%
k168 1
 
1.1%
Other values (84) 84
89.4%
2025-05-17T12:40:49.082858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 79
21.0%
N 48
12.7%
K 46
12.2%
0 40
10.6%
2 35
9.3%
9 33
8.8%
7 23
 
6.1%
8 19
 
5.0%
5 17
 
4.5%
3 13
 
3.4%
Other values (3) 24
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 79
21.0%
N 48
12.7%
K 46
12.2%
0 40
10.6%
2 35
9.3%
9 33
8.8%
7 23
 
6.1%
8 19
 
5.0%
5 17
 
4.5%
3 13
 
3.4%
Other values (3) 24
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 79
21.0%
N 48
12.7%
K 46
12.2%
0 40
10.6%
2 35
9.3%
9 33
8.8%
7 23
 
6.1%
8 19
 
5.0%
5 17
 
4.5%
3 13
 
3.4%
Other values (3) 24
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 79
21.0%
N 48
12.7%
K 46
12.2%
0 40
10.6%
2 35
9.3%
9 33
8.8%
7 23
 
6.1%
8 19
 
5.0%
5 17
 
4.5%
3 13
 
3.4%
Other values (3) 24
 
6.4%

Population
Categorical

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
NET
48 
KUR
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters282
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKUR
2nd rowKUR
3rd rowKUR
4th rowKUR
5th rowKUR

Common Values

ValueCountFrequency (%)
NET 48
51.1%
KUR 46
48.9%

Length

2025-05-17T12:40:49.169546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T12:40:49.227701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
net 48
51.1%
kur 46
48.9%

Most occurring characters

ValueCountFrequency (%)
N 48
17.0%
E 48
17.0%
T 48
17.0%
K 46
16.3%
U 46
16.3%
R 46
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 48
17.0%
E 48
17.0%
T 48
17.0%
K 46
16.3%
U 46
16.3%
R 46
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 48
17.0%
E 48
17.0%
T 48
17.0%
K 46
16.3%
U 46
16.3%
R 46
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 48
17.0%
E 48
17.0%
T 48
17.0%
K 46
16.3%
U 46
16.3%
R 46
16.3%

Treatment
Categorical

High correlation 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
CONTROL
73 
SUPP
21 

Length

Max length7
Median length7
Mean length6.3297872
Min length4

Characters and Unicode

Total characters595
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONTROL
2nd rowCONTROL
3rd rowCONTROL
4th rowCONTROL
5th rowCONTROL

Common Values

ValueCountFrequency (%)
CONTROL 73
77.7%
SUPP 21
 
22.3%

Length

2025-05-17T12:40:49.305174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T12:40:49.371595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
control 73
77.7%
supp 21
 
22.3%

Most occurring characters

ValueCountFrequency (%)
O 146
24.5%
C 73
12.3%
N 73
12.3%
T 73
12.3%
R 73
12.3%
L 73
12.3%
P 42
 
7.1%
S 21
 
3.5%
U 21
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 146
24.5%
C 73
12.3%
N 73
12.3%
T 73
12.3%
R 73
12.3%
L 73
12.3%
P 42
 
7.1%
S 21
 
3.5%
U 21
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 146
24.5%
C 73
12.3%
N 73
12.3%
T 73
12.3%
R 73
12.3%
L 73
12.3%
P 42
 
7.1%
S 21
 
3.5%
U 21
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 146
24.5%
C 73
12.3%
N 73
12.3%
T 73
12.3%
R 73
12.3%
L 73
12.3%
P 42
 
7.1%
S 21
 
3.5%
U 21
 
3.5%

total_flower
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2340426
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:49.440425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q310
95-th percentile21.4
Maximum34
Range33
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1337838
Coefficient of variation (CV)0.98614071
Kurtosis3.7331803
Mean7.2340426
Median Absolute Deviation (MAD)4
Skewness1.8505115
Sum680
Variance50.890872
MonotonicityNot monotonic
2025-05-17T12:40:49.537689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 19
20.2%
2 11
11.7%
5 8
 
8.5%
7 8
 
8.5%
4 6
 
6.4%
6 5
 
5.3%
3 5
 
5.3%
11 4
 
4.3%
9 4
 
4.3%
10 4
 
4.3%
Other values (12) 20
21.3%
ValueCountFrequency (%)
1 19
20.2%
2 11
11.7%
3 5
 
5.3%
4 6
 
6.4%
5 8
8.5%
6 5
 
5.3%
7 8
8.5%
8 3
 
3.2%
9 4
 
4.3%
10 4
 
4.3%
ValueCountFrequency (%)
34 1
 
1.1%
32 1
 
1.1%
30 1
 
1.1%
28 1
 
1.1%
24 1
 
1.1%
20 1
 
1.1%
19 1
 
1.1%
16 4
4.3%
15 2
2.1%
13 1
 
1.1%

Marked_flower
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.212766
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:49.622939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8011992
Coefficient of variation (CV)0.87189644
Kurtosis2.8288638
Mean3.212766
Median Absolute Deviation (MAD)1
Skewness1.717077
Sum302
Variance7.846717
MonotonicityNot monotonic
2025-05-17T12:40:49.779609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 33
35.1%
2 19
20.2%
3 13
 
13.8%
5 8
 
8.5%
4 6
 
6.4%
6 4
 
4.3%
8 3
 
3.2%
9 2
 
2.1%
7 2
 
2.1%
13 2
 
2.1%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
1 33
35.1%
2 19
20.2%
3 13
 
13.8%
4 6
 
6.4%
5 8
 
8.5%
6 4
 
4.3%
7 2
 
2.1%
8 3
 
3.2%
9 2
 
2.1%
10 1
 
1.1%
ValueCountFrequency (%)
13 2
 
2.1%
12 1
 
1.1%
10 1
 
1.1%
9 2
 
2.1%
8 3
 
3.2%
7 2
 
2.1%
6 4
 
4.3%
5 8
8.5%
4 6
6.4%
3 13
13.8%

Unmarked_flower
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0212766
Minimum0
Maximum26
Zeros25
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:49.860816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile14.35
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0053277
Coefficient of variation (CV)1.2447111
Kurtosis4.6425293
Mean4.0212766
Median Absolute Deviation (MAD)2
Skewness2.0314648
Sum378
Variance25.053306
MonotonicityNot monotonic
2025-05-17T12:40:49.937550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25
26.6%
3 11
11.7%
1 11
11.7%
2 10
 
10.6%
5 9
 
9.6%
4 7
 
7.4%
6 5
 
5.3%
10 2
 
2.1%
7 2
 
2.1%
14 2
 
2.1%
Other values (10) 10
 
10.6%
ValueCountFrequency (%)
0 25
26.6%
1 11
11.7%
2 10
 
10.6%
3 11
11.7%
4 7
 
7.4%
5 9
 
9.6%
6 5
 
5.3%
7 2
 
2.1%
8 1
 
1.1%
9 1
 
1.1%
ValueCountFrequency (%)
26 1
1.1%
20 1
1.1%
18 1
1.1%
17 1
1.1%
15 1
1.1%
14 2
2.1%
13 1
1.1%
12 1
1.1%
11 1
1.1%
10 2
2.1%

Supp_flower
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53191489
Minimum0
Maximum8
Zeros72
Zeros (%)76.6%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:50.016179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3733416
Coefficient of variation (CV)2.5818823
Kurtosis14.733982
Mean0.53191489
Median Absolute Deviation (MAD)0
Skewness3.6475058
Sum50
Variance1.8860673
MonotonicityNot monotonic
2025-05-17T12:40:50.089454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 72
76.6%
1 13
 
13.8%
3 3
 
3.2%
2 2
 
2.1%
5 1
 
1.1%
7 1
 
1.1%
4 1
 
1.1%
8 1
 
1.1%
ValueCountFrequency (%)
0 72
76.6%
1 13
 
13.8%
2 2
 
2.1%
3 3
 
3.2%
4 1
 
1.1%
5 1
 
1.1%
7 1
 
1.1%
8 1
 
1.1%
ValueCountFrequency (%)
8 1
 
1.1%
7 1
 
1.1%
5 1
 
1.1%
4 1
 
1.1%
3 3
 
3.2%
2 2
 
2.1%
1 13
 
13.8%
0 72
76.6%

Marked_fruit
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5851064
Minimum0
Maximum7
Zeros25
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:50.148689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6291008
Coefficient of variation (CV)1.0277549
Kurtosis1.4564093
Mean1.5851064
Median Absolute Deviation (MAD)1
Skewness1.3541876
Sum149
Variance2.6539693
MonotonicityNot monotonic
2025-05-17T12:40:50.216235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 33
35.1%
0 25
26.6%
2 17
18.1%
4 6
 
6.4%
3 6
 
6.4%
6 3
 
3.2%
5 3
 
3.2%
7 1
 
1.1%
ValueCountFrequency (%)
0 25
26.6%
1 33
35.1%
2 17
18.1%
3 6
 
6.4%
4 6
 
6.4%
5 3
 
3.2%
6 3
 
3.2%
7 1
 
1.1%
ValueCountFrequency (%)
7 1
 
1.1%
6 3
 
3.2%
5 3
 
3.2%
4 6
 
6.4%
3 6
 
6.4%
2 17
18.1%
1 33
35.1%
0 25
26.6%

Unmarked_fruit
Categorical

High correlation  Missing 

Distinct5
Distinct (%)5.4%
Missing1
Missing (%)1.1%
Memory size884.0 B
0.0
48 
1.0
33 
3.0
2.0
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters279
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row2.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48
51.1%
1.0 33
35.1%
3.0 6
 
6.4%
2.0 5
 
5.3%
5.0 1
 
1.1%
(Missing) 1
 
1.1%

Length

2025-05-17T12:40:50.309556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T12:40:50.379431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
51.6%
1.0 33
35.5%
3.0 6
 
6.5%
2.0 5
 
5.4%
5.0 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 141
50.5%
. 93
33.3%
1 33
 
11.8%
3 6
 
2.2%
2 5
 
1.8%
5 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 141
50.5%
. 93
33.3%
1 33
 
11.8%
3 6
 
2.2%
2 5
 
1.8%
5 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 141
50.5%
. 93
33.3%
1 33
 
11.8%
3 6
 
2.2%
2 5
 
1.8%
5 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 141
50.5%
. 93
33.3%
1 33
 
11.8%
3 6
 
2.2%
2 5
 
1.8%
5 1
 
0.4%

Total_fruit
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct10
Distinct (%)10.8%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.311828
Minimum0
Maximum9
Zeros13
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:50.441979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0798124
Coefficient of variation (CV)0.89963976
Kurtosis1.7142545
Mean2.311828
Median Absolute Deviation (MAD)1
Skewness1.3951485
Sum215
Variance4.3256194
MonotonicityNot monotonic
2025-05-17T12:40:50.513720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 27
28.7%
2 23
24.5%
0 13
13.8%
3 11
11.7%
4 6
 
6.4%
5 4
 
4.3%
6 3
 
3.2%
7 3
 
3.2%
9 2
 
2.1%
8 1
 
1.1%
(Missing) 1
 
1.1%
ValueCountFrequency (%)
0 13
13.8%
1 27
28.7%
2 23
24.5%
3 11
11.7%
4 6
 
6.4%
5 4
 
4.3%
6 3
 
3.2%
7 3
 
3.2%
8 1
 
1.1%
9 2
 
2.1%
ValueCountFrequency (%)
9 2
 
2.1%
8 1
 
1.1%
7 3
 
3.2%
6 3
 
3.2%
5 4
 
4.3%
4 6
 
6.4%
3 11
11.7%
2 23
24.5%
1 27
28.7%
0 13
13.8%

Average of Flower_size
Real number (ℝ)

Unique 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.832965
Minimum24.03904
Maximum82.6458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:50.600867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.03904
5-th percentile28.938152
Q137.413379
median45.1191
Q351.23104
95-th percentile65.982536
Maximum82.6458
Range58.60676
Interquartile range (IQR)13.817661

Descriptive statistics

Standard deviation12.063261
Coefficient of variation (CV)0.26320054
Kurtosis1.2844755
Mean45.832965
Median Absolute Deviation (MAD)6.9606167
Skewness0.91412015
Sum4308.2987
Variance145.52227
MonotonicityNot monotonic
2025-05-17T12:40:50.713311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.6324 1
 
1.1%
45.224 1
 
1.1%
36.642 1
 
1.1%
36.01908 1
 
1.1%
51.3162 1
 
1.1%
42.92665 1
 
1.1%
34.7259 1
 
1.1%
40.5669 1
 
1.1%
24.03904 1
 
1.1%
34.77956 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
24.03904 1
1.1%
24.50226667 1
1.1%
26.676 1
1.1%
27.4122 1
1.1%
27.64883333 1
1.1%
29.6324 1
1.1%
30.6747 1
1.1%
31.7393 1
1.1%
31.9566 1
1.1%
32.2188 1
1.1%
ValueCountFrequency (%)
82.6458 1
1.1%
81.989 1
1.1%
81.0836 1
1.1%
75.9096 1
1.1%
67.06466667 1
1.1%
65.39985 1
1.1%
64.848 1
1.1%
64.48946 1
1.1%
64.09995 1
1.1%
64.05766667 1
1.1%
Distinct93
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5363374
Minimum1.58137
Maximum6.67128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:50.837832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.58137
5-th percentile2.0691765
Q13.0065292
median3.5115722
Q34.0837897
95-th percentile5.108344
Maximum6.67128
Range5.08991
Interquartile range (IQR)1.0772605

Descriptive statistics

Standard deviation0.92037897
Coefficient of variation (CV)0.26026334
Kurtosis0.94613478
Mean3.5363374
Median Absolute Deviation (MAD)0.53926658
Skewness0.54861083
Sum332.41571
Variance0.84709745
MonotonicityNot monotonic
2025-05-17T12:40:50.954918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.556036 2
 
2.1%
4.29786 1
 
1.1%
3.153465 1
 
1.1%
3.3880752 1
 
1.1%
3.71606 1
 
1.1%
3.611496 1
 
1.1%
3.601684 1
 
1.1%
4.086082 1
 
1.1%
3.63258 1
 
1.1%
2.08521 1
 
1.1%
Other values (83) 83
88.3%
ValueCountFrequency (%)
1.58137 1
1.1%
1.832787 1
1.1%
1.954228 1
1.1%
1.978792 1
1.1%
2.0394 1
1.1%
2.08521 1
1.1%
2.201400083 1
1.1%
2.242 1
1.1%
2.425437 1
1.1%
2.426432 1
1.1%
ValueCountFrequency (%)
6.67128 1
1.1%
5.97516 1
1.1%
5.649018 1
1.1%
5.360754 1
1.1%
5.26014 1
1.1%
5.026607714 1
1.1%
4.92147 1
1.1%
4.657213333 1
1.1%
4.581713 1
1.1%
4.578324 1
1.1%

Average of bp_area
Real number (ℝ)

Unique 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8784794
Minimum5.1775
Maximum11.784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:51.060907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.1775
5-th percentile6.02025
Q17.087375
median7.8263333
Q38.391775
95-th percentile10.41232
Maximum11.784
Range6.6065
Interquartile range (IQR)1.3044

Descriptive statistics

Standard deviation1.283971
Coefficient of variation (CV)0.16297193
Kurtosis1.0407379
Mean7.8784794
Median Absolute Deviation (MAD)0.644125
Skewness0.72695636
Sum740.57707
Variance1.6485815
MonotonicityNot monotonic
2025-05-17T12:40:51.163809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.59 1
 
1.1%
8.1345 1
 
1.1%
7.903 1
 
1.1%
6.9836 1
 
1.1%
7.445 1
 
1.1%
6.58775 1
 
1.1%
7.652 1
 
1.1%
7.841 1
 
1.1%
7.7836 1
 
1.1%
8.1333 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
5.1775 1
1.1%
5.452 1
1.1%
5.735333333 1
1.1%
5.7765 1
1.1%
5.978 1
1.1%
6.043 1
1.1%
6.1205 1
1.1%
6.201 1
1.1%
6.292 1
1.1%
6.314 1
1.1%
ValueCountFrequency (%)
11.784 1
1.1%
11.481 1
1.1%
11.095 1
1.1%
10.928 1
1.1%
10.43 1
1.1%
10.4028 1
1.1%
10.3045 1
1.1%
10.043 1
1.1%
9.664 1
1.1%
9.588 1
1.1%

Fruitset_total
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct36
Distinct (%)38.7%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.4559163
Minimum0
Maximum2
Zeros13
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:51.277701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.18181818
median0.38461538
Q30.6
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0.41818182

Descriptive statistics

Standard deviation0.38967027
Coefficient of variation (CV)0.85469694
Kurtosis3.5930703
Mean0.4559163
Median Absolute Deviation (MAD)0.21538462
Skewness1.4993228
Sum42.400216
Variance0.15184292
MonotonicityNot monotonic
2025-05-17T12:40:51.384607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 13
13.8%
1 13
13.8%
0.5 12
 
12.8%
0.3333333333 8
 
8.5%
0.2 4
 
4.3%
0.1666666667 4
 
4.3%
0.4 3
 
3.2%
0.2857142857 2
 
2.1%
2 2
 
2.1%
0.4285714286 2
 
2.1%
Other values (26) 30
31.9%
ValueCountFrequency (%)
0 13
13.8%
0.08333333333 1
 
1.1%
0.125 2
 
2.1%
0.1333333333 1
 
1.1%
0.1428571429 1
 
1.1%
0.1578947368 1
 
1.1%
0.1666666667 4
 
4.3%
0.1818181818 1
 
1.1%
0.1875 1
 
1.1%
0.2 4
 
4.3%
ValueCountFrequency (%)
2 2
 
2.1%
1 13
13.8%
0.875 1
 
1.1%
0.8571428571 1
 
1.1%
0.8 1
 
1.1%
0.75 2
 
2.1%
0.6666666667 1
 
1.1%
0.6363636364 1
 
1.1%
0.6 2
 
2.1%
0.5714285714 1
 
1.1%

Fruitset_marked
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50879608
Minimum0
Maximum1
Zeros25
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:51.476632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q30.95833333
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95833333

Descriptive statistics

Standard deviation0.38379401
Coefficient of variation (CV)0.75431793
Kurtosis-1.4203738
Mean0.50879608
Median Absolute Deviation (MAD)0.5
Skewness-0.085122215
Sum47.826832
Variance0.14729784
MonotonicityNot monotonic
2025-05-17T12:40:51.567843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25
26.6%
1 24
25.5%
0.5 12
12.8%
0.6666666667 7
 
7.4%
0.3333333333 4
 
4.3%
0.8 3
 
3.2%
0.75 3
 
3.2%
0.6 3
 
3.2%
0.4 2
 
2.1%
0.2222222222 1
 
1.1%
Other values (10) 10
 
10.6%
ValueCountFrequency (%)
0 25
26.6%
0.1538461538 1
 
1.1%
0.1666666667 1
 
1.1%
0.2 1
 
1.1%
0.2222222222 1
 
1.1%
0.25 1
 
1.1%
0.3333333333 4
 
4.3%
0.4 2
 
2.1%
0.4285714286 1
 
1.1%
0.4444444444 1
 
1.1%
ValueCountFrequency (%)
1 24
25.5%
0.8333333333 1
 
1.1%
0.8 3
 
3.2%
0.75 3
 
3.2%
0.7142857143 1
 
1.1%
0.6666666667 7
 
7.4%
0.625 1
 
1.1%
0.6 3
 
3.2%
0.5384615385 1
 
1.1%
0.5 12
12.8%

Sum of seed_marked
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct52
Distinct (%)55.9%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean29.849462
Minimum0
Maximum168
Zeros25
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:51.673808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q346
95-th percentile114.6
Maximum168
Range168
Interquartile range (IQR)46

Descriptive statistics

Standard deviation36.471831
Coefficient of variation (CV)1.2218589
Kurtosis2.5276898
Mean29.849462
Median Absolute Deviation (MAD)17
Skewness1.6153713
Sum2776
Variance1330.1945
MonotonicityNot monotonic
2025-05-17T12:40:51.789731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
26.6%
7 4
 
4.3%
8 3
 
3.2%
2 3
 
3.2%
3 3
 
3.2%
44 2
 
2.1%
26 2
 
2.1%
19 2
 
2.1%
15 2
 
2.1%
34 2
 
2.1%
Other values (42) 45
47.9%
ValueCountFrequency (%)
0 25
26.6%
2 3
 
3.2%
3 3
 
3.2%
4 1
 
1.1%
5 1
 
1.1%
6 1
 
1.1%
7 4
 
4.3%
8 3
 
3.2%
9 1
 
1.1%
14 1
 
1.1%
ValueCountFrequency (%)
168 1
1.1%
133 1
1.1%
131 1
1.1%
127 1
1.1%
117 1
1.1%
113 1
1.1%
101 1
1.1%
84 1
1.1%
81 1
1.1%
72 1
1.1%

Seeds_unmarked
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct31
Distinct (%)35.6%
Missing7
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean15.091954
Minimum0
Maximum143
Zeros49
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:51.883350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q322.5
95-th percentile64.5
Maximum143
Range143
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation25.897217
Coefficient of variation (CV)1.7159618
Kurtosis7.0924132
Mean15.091954
Median Absolute Deviation (MAD)0
Skewness2.3861495
Sum1313
Variance670.66586
MonotonicityNot monotonic
2025-05-17T12:40:51.983624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 49
52.1%
9 4
 
4.3%
5 3
 
3.2%
53 2
 
2.1%
20 2
 
2.1%
27 2
 
2.1%
54 1
 
1.1%
2 1
 
1.1%
52 1
 
1.1%
61 1
 
1.1%
Other values (21) 21
22.3%
(Missing) 7
 
7.4%
ValueCountFrequency (%)
0 49
52.1%
2 1
 
1.1%
5 3
 
3.2%
6 1
 
1.1%
8 1
 
1.1%
9 4
 
4.3%
12 1
 
1.1%
18 1
 
1.1%
19 1
 
1.1%
20 2
 
2.1%
ValueCountFrequency (%)
143 1
1.1%
102 1
1.1%
69 1
1.1%
67 1
1.1%
66 1
1.1%
61 1
1.1%
58 1
1.1%
54 1
1.1%
53 2
2.1%
52 1
1.1%

Total_seed
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct51
Distinct (%)59.3%
Missing8
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean43.523256
Minimum0
Maximum209
Zeros14
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size884.0 B
2025-05-17T12:40:52.087857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median32.5
Q359.5
95-th percentile133
Maximum209
Range209
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation45.993024
Coefficient of variation (CV)1.0567459
Kurtosis1.9503593
Mean43.523256
Median Absolute Deviation (MAD)25.5
Skewness1.4736901
Sum3743
Variance2115.3583
MonotonicityNot monotonic
2025-05-17T12:40:52.189439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
14.9%
8 4
 
4.3%
7 3
 
3.2%
41 2
 
2.1%
30 2
 
2.1%
2 2
 
2.1%
15 2
 
2.1%
133 2
 
2.1%
6 2
 
2.1%
12 2
 
2.1%
Other values (41) 51
54.3%
(Missing) 8
 
8.5%
ValueCountFrequency (%)
0 14
14.9%
2 2
 
2.1%
6 2
 
2.1%
7 3
 
3.2%
8 4
 
4.3%
9 2
 
2.1%
12 2
 
2.1%
15 2
 
2.1%
17 1
 
1.1%
21 1
 
1.1%
ValueCountFrequency (%)
209 1
1.1%
171 1
1.1%
168 1
1.1%
150 1
1.1%
133 2
2.1%
131 1
1.1%
128 1
1.1%
127 1
1.1%
106 1
1.1%
102 1
1.1%

Interactions

2025-05-17T12:40:47.035247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:29.656064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:30.767543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:34.080469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:35.147598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:36.262971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:37.295094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:38.678601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:39.644840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:40.591534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:41.972222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:43.089666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:44.224003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:45.430789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:47.109688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:29.745028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:30.843928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:34.156379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:35.230784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:36.332753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:37.764954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:38.738442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:39.712029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:40.670446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:42.069787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:43.160804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:44.309743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:45.516343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:47.176648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:29.827444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:30.910530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:34.210851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:35.308387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:36.393977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:37.835235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:38.798852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:39.776418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:40.736307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:42.143705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:43.236580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:44.397778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:45.599586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:47.245930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:29.908521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:30.965797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:34.280533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:35.389771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T12:40:37.907558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:38.867606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:39.842999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:40.803999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:42.224373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:43.301533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:44.481973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:45.693376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:47.322971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:29.994032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T12:40:39.976101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T12:40:44.653486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T12:40:41.909431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:43.011435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:44.150519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:45.357551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T12:40:46.952635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-17T12:40:52.282018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Average of Flower_sizeAverage of bp_areaAverage of tunnel_volume_cmFruitset_markedFruitset_totalMarked_flowerMarked_fruitPopulationSeeds_unmarkedSum of seed_markedSupp_flowerTotal_fruitTotal_seedTreatmentUnmarked_flowerUnmarked_fruittotal_flower
Average of Flower_size1.0000.0490.4910.022-0.0010.0040.0500.452-0.1870.0790.231-0.069-0.0280.080-0.1710.000-0.110
Average of bp_area0.0491.000-0.0540.030-0.061-0.0050.0060.391-0.005-0.011-0.015-0.061-0.0150.469-0.0050.067-0.032
Average of tunnel_volume_cm0.491-0.0541.000-0.055-0.0850.1260.0200.2280.0660.0720.023-0.0080.1730.000-0.0170.0000.032
Fruitset_marked0.0220.030-0.0551.0000.642-0.0860.5600.153-0.1830.5550.1970.3390.3220.105-0.0660.000-0.089
Fruitset_total-0.001-0.061-0.0850.6421.000-0.1780.2470.1920.1750.2920.2230.2950.2970.286-0.4100.136-0.364
Marked_flower0.004-0.0050.126-0.086-0.1781.0000.7150.1750.3340.560-0.1330.7440.6410.0000.6580.1970.853
Marked_fruit0.0500.0060.0200.5600.2470.7151.0000.0000.0990.8530.0510.8620.7110.0000.4770.2060.605
Population0.4520.3910.2280.1530.1920.1750.0001.0000.3270.0000.4240.1710.2950.4910.2950.3050.082
Seeds_unmarked-0.187-0.0050.066-0.1830.1750.3340.0990.3271.000-0.017-0.0350.5030.4650.0000.2810.5080.321
Sum of seed_marked0.079-0.0110.0720.5550.2920.5600.8530.000-0.0171.0000.0070.6910.8120.0000.3510.2270.453
Supp_flower0.231-0.0150.0230.1970.223-0.1330.0510.424-0.0350.0071.000-0.024-0.0490.945-0.2150.000-0.208
Total_fruit-0.069-0.061-0.0080.3390.2950.7440.8620.1710.5030.691-0.0241.0000.7850.0000.5990.5130.702
Total_seed-0.028-0.0150.1730.3220.2970.6410.7110.2950.4650.812-0.0490.7851.0000.1790.3960.4270.521
Treatment0.0800.4690.0000.1050.2860.0000.0000.4910.0000.0000.9450.0000.1791.0000.1570.0190.049
Unmarked_flower-0.171-0.005-0.017-0.066-0.4100.6580.4770.2950.2810.351-0.2150.5990.3960.1571.0000.3500.943
Unmarked_fruit0.0000.0670.0000.0000.1360.1970.2060.3050.5080.2270.0000.5130.4270.0190.3501.0000.272
total_flower-0.110-0.0320.032-0.089-0.3640.8530.6050.0820.3210.453-0.2080.7020.5210.0490.9430.2721.000

Missing values

2025-05-17T12:40:48.138716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T12:40:48.304314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-17T12:40:48.482323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GenotypePopulationTreatmenttotal_flowerMarked_flowerUnmarked_flowerSupp_flowerMarked_fruitUnmarked_fruitTotal_fruitAverage of Flower_sizeAverage of tunnel_volume_cmAverage of bp_areaFruitset_totalFruitset_markedSum of seed_markedSeeds_unmarkedTotal_seed
0K135KURCONTROL413011.02.029.632403.7160606.590000.5000001.008.0NaNNaN
1K136KURCONTROL220000.00.045.224004.2978608.134500.0000000.000.00.00.0
2K139KURCONTROL615011.02.036.642003.1534657.903000.3333331.0019.0NaNNaN
3K140KURCONTROL1156042.06.036.019083.3880756.983600.5454550.80117.054.0171.0
4K142KURCONTROL413000.00.051.316203.6114967.445000.0000000.000.00.00.0
5K147KURCONTROL541031.04.042.926653.6016846.587750.8000000.7554.09.063.0
6K155KURCONTROL110010.01.034.725903.6325807.652001.0000001.0040.00.040.0
7K157KURCONTROL110001.01.040.566904.0860827.841001.0000000.000.030.030.0
8K159KURCONTROL1055020.02.024.039042.0852107.783600.2000000.4015.00.015.0
9K168KURCONTROL16106063.09.034.779562.7828708.133300.5625000.6066.0143.0209.0
GenotypePopulationTreatmenttotal_flowerMarked_flowerUnmarked_flowerSupp_flowerMarked_fruitUnmarked_fruitTotal_fruitAverage of Flower_sizeAverage of tunnel_volume_cmAverage of bp_areaFruitset_totalFruitset_markedSum of seed_markedSeeds_unmarkedTotal_seed
84N196NETSUPP725211.02.052.4692004.5061248.5960000.2857140.500000NaN33.0NaN
85N197NETCONTROL624020.02.082.6458004.3184848.9870000.3333331.00000028.00.028.0
86N198NETSUPP1183861.07.064.0999503.0885108.4847500.6363640.75000048.058.0106.0
87N199NETCONTROL523001.01.047.0015503.0253317.9720000.2000000.0000000.029.029.0
88N200NETSUPP532321.03.037.6180673.6955277.7353330.6000000.6666679.024.033.0
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